Source code for pymc_marketing.mmm.preprocessing

#   Copyright 2024 The PyMC Labs Developers
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
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#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Preprocessing methods for the Marketing Mix Model."""

from collections.abc import Callable
from typing import Any

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MaxAbsScaler, StandardScaler

__all__ = [
    "preprocessing_method_X",
    "preprocessing_method_y",
    "MaxAbsScaleTarget",
    "MaxAbsScaleChannels",
    "StandardizeControls",
]




def preprocessing_method_X(method: Callable) -> Callable:
    if not hasattr(method, "_tags"):
        method._tags = {}  # type: ignore
    method._tags["preprocessing_X"] = True  # type: ignore
    return method





def preprocessing_method_y(method: Callable) -> Callable:
    if not hasattr(method, "_tags"):
        method._tags = {}  # type: ignore
    method._tags["preprocessing_y"] = True  # type: ignore
    return method





class MaxAbsScaleTarget:
    target_transformer: Pipeline



    @preprocessing_method_y
    def max_abs_scale_target_data(
        self, data: pd.Series | np.ndarray
    ) -> np.ndarray | pd.Series:
        if isinstance(data, pd.Series):
            data = data.to_numpy()

        target_vector = data.reshape(-1, 1)
        transformers = [("scaler", MaxAbsScaler())]
        pipeline = Pipeline(steps=transformers)
        self.target_transformer: Pipeline = pipeline.fit(X=target_vector)
        data = self.target_transformer.transform(X=target_vector).flatten()
        return data






class MaxAbsScaleChannels:
    channel_columns: list[str] | tuple[str]



    @preprocessing_method_X
    def max_abs_scale_channel_data(self, data: pd.DataFrame) -> pd.DataFrame:
        data_cp = data.copy()
        channel_data: pd.DataFrame | pd.Series[Any] = data_cp[self.channel_columns]
        transformers = [("scaler", MaxAbsScaler())]
        pipeline: Pipeline = Pipeline(steps=transformers)
        self.channel_transformer: Pipeline = pipeline.fit(X=channel_data.to_numpy())
        data_cp[self.channel_columns] = self.channel_transformer.transform(
            channel_data.to_numpy()
        )
        return data_cp






class StandardizeControls:
    control_columns: list[str]  # TODO: Handle Optional[List[str]]



    @preprocessing_method_X
    def standardize_control_data(self, data: pd.DataFrame) -> pd.DataFrame:
        control_data: pd.DataFrame = data[self.control_columns]
        transformers = [("scaler", StandardScaler())]
        pipeline: Pipeline = Pipeline(steps=transformers)
        self.control_transformer: Pipeline = pipeline.fit(X=control_data.to_numpy())
        data[self.control_columns] = self.control_transformer.transform(
            control_data.to_numpy()
        )
        return data